QoE based admission control

ABSTRACT

In one example, a Cable Modem Termination System (CMTS) analyzes received service flow traffic to estimate Quality of Experience (QoE) at the endpoints. An admission control system on the CMTS uses the QoE estimate to determine whether to admit new service flows.

BACKGROUND

Cable operators have widely deployed high-speed data services on cabletelevision systems. These data services allow subscriber-side devices,such as personal computers, to communicate over an ordinary cable TVnetwork Hybrid Fiber Coax (HFC) cable plant. A Cable Modem TerminationSystem (CMTS) connects the cable TV network to a data network, such asthe Internet. The Data Over Cable Service Interface Specification(DOCSIS) is one of the cable modem standards used for transferring dataover the cable TV network.

The CMTS includes an admission control component that controls whether anew connection can be admitted into the cable network. This admissioncontrol component determines current bandwidth utilization by summingpre-computed bandwidth estimates for existing connections and thencomparing the current bandwidth utilization to a pre-computed availablebandwidth. If the difference between the determined current bandwidthand the available bandwidth exceeds a preset fixed margin, the newconnection is admitted.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for admitting connections according to aQuality of Experience (QoE) estimation.

FIG. 2 illustrates one example of a Cable Modem Termination System(CMTS) according to the system shown in FIG. 1.

FIG. 3 is a graph illustrating an analysis that can be performed by theCMTS shown in FIG. 2 to determine when to stop admitting connections.

FIG. 4 illustrates how the CMTS shown in FIG. 2 conducts admissioncontrol according to QoE estimation.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

In one example, a Cable Modem Termination System (CMTS) analyzesreceived service flow traffic to estimate Quality of Experience (QoE) atthe endpoints. An admission control system on the CMTS uses the QoEestimate to determine whether to admit new service flows.

Description

FIG. 1 illustrates a system for admitting connections according to aQuality of Experience (QoE) estimation.

The system 100 includes an admission control component 14 and a QoEestimation component 16 operating on the Cable Modem Termination System(CMTS) 12. The component 16 analyzes packet traffic for existingconnections, such as connections extending between the data network andthe HFC network and connections extending between devices behind the HFCnetwork, to estimate the QoE at the endpoints associated with theexisting connections. The component 16 passes raw measurement dataand/or measurement determinations 15 based on the analysis to theadmission control component 14 for use in determining whether to admitor deny requests for new connections.

The use of measurement-based data 15 for call admission by the CMTS 12can be contrasted with the use of pre-computed bandwidth estimates.Whereas pre-computed bandwidth estimates limit the maximum number ofcalls to a fixed amount, namely total bandwidth over the pre-computedbandwidth value, the system 100 admits as many connections as can behandled without QoE disruptions.

It should also be appreciated that the example CMTS 12 bases calladmission control on QoE for users of the call. This is different thanbasing call admission solely on a comparison between currently utilizedbandwidth and total available bandwidth, because users can have a poorquality of experience even when the system has more than a set thresholdamount of bandwidth remaining. For example, even before a margin foravailable bandwidth is met, it is possible for a user to experiencedelays and other disruptions, which could result in choppy video in thecase of a user viewing a video stream.

In one example, the component 16 determines QoE by measuring one or moremetrics of packet traffic on the CMTS 12 (such as packet loss or packetdelay) and comparing the measurements to a threshold. These thresholdscan be set based on an empirical study of how much packet loss or packetdelay can be reached before customers, e.g. listeners, viewers, users,etc., become unhappy with the provided service, or set by simplypredicting a threshold amount of packet loss or packet delay that islikely to mark significant discernable effects at the endpoints.Accordingly, these packet traffic measurements and thresholds are usedto estimate QoE and thus are representative of the user's experience atthe endpoint(s). As described throughout, QoE can be estimated using anymeasurement packet metric of packet traffic on the CMTS 12, not justpacket loss and packet delay.

In exemplary embodiments, different packet flow metrics can be used toestimate QoE alone or in combination with a bandwidth comparison such asbut not limited to: monitoring service flow queue depth on the CMTS,counting an amount of re-transmissions triggered by a Cisco VisualQuality of Experience (VQE) agent, monitoring packets dropped by theservice flow queues on the CMTS, and monitoring one-way packet latencyfor packets being processed by the CMTS using features of the CiscoInternetwork Operating System (IOS) software associated with CiscoInternet Protocol (IP) Service Level Agreement (SLA) analysis. The QoEestimation component 16 measures these metrics using the service flowqueue monitor 17, the re-transmission monitor 18, the packet dropmonitor 19, and the one-way packet delay monitor 20. The QoE estimationcomponent 16 uses the bandwidth usage monitor 21 to measure actualbandwidth usage on the RF channels, which can be used for purposes ofcomparing measured bandwidth load to total available bandwidth toestimate QoE.

In example embodiments, the system 100 can utilize various packet flowmetrics to estimate QoE. Any such packet flow metrics may characterizethe connections on either a flow-by-flow basis or some other basis thatallows QoE to be estimated separately for a first subset of theconnections and a second different subset of the connections (see thenext paragraph). Also, it should be understood that the CMTS 12 may useonly one packet flow metric to estimate QoE, or a combination of thepacket flow metrics.

Furthermore, it should be appreciated that, in example embodiments, atleast some of the above described packet flow metrics can approximateQoE on a connection-by-connection basis. For example, if poor quality ofexperience is identified in a subset of the existing connections, forexample just one of the existing connections, new connections can bedenied even though a system wide approach may indicate that bandwidthshould still be available. If a comparison between measured packet loadand total bandwidth available is used for estimating QoE, then thiscomparison can be performed on a per-service group basis or on aper-interface basis (the CMTS 12 can include both narrowband andwideband interfaces) to identify congestion that might be affecting theexperience of a subset of users.

It should be understood there can be advantages to utilizing QoE basedcall admission in addition to pre-computed bandwidth estimates. Forexample, the system 100 could deny new calls upon detecting either of 1)the difference between the determined current bandwidth (usingpre-computed values) and the available bandwidth exceeding a presetfixed margin or 2) packet measurements indicating a QoE disruption.

In an embodiment, a new connection may be accepted in conjunction withaltering an existing connection. For example, if a QoE disruption isdetected in conjunction with a request for a new connection, the newconnection can be admitted by changing any parameter of an existingconnection such as disconnecting a random voice call, changing arandomly selected voice call from constant bit rate to best effort,changing a high definition video stream to standard definition, etc. Itis also possible to admit the new connection with a modification, forexample, admitting a standard definition video stream for the requestedcontent instead of a high definition video stream for the requestedcontent.

The above description discusses measuring packets at an intermediary todetect “QoE disruption” at an endpoint. The term “QoE disruption” asused throughout refers to both immediate disruptions, e.g. the packetanalysis indicates that the endpoint is likely currently observing poorQoE, or expected disruptions, e.g. the packet analysis indicates thatadditional admission into the system 100 will likely result in poor QoEat the endpoint. In either case, a threshold can be used to identify theQoE disruption. For example, if the packet analysis identifies one-waypacket delay exceeding a set threshold amount, the packet analysis hasdetected a QoE disruption.

Although the admission control component 14 and the QoE estimationcomponent 16 are illustrated to be operating locally on the CMTS 12, itshould be understood that either or both of these components can belocated off box. For example, an external device could monitor packetflow metrics and send the raw measurement data (or determinations basedon the raw measurement data) to the CMTS 12. Alternatively, the externaldevice could send the raw data and/or determinations to a policy serverthrough an application manger interface. The CMTS 12 would consult thepolicy server for approval for each new connection.

Having provided an overview of the benefits of basing call admission onQoE estimates with reference to FIG. 1, reference will now be made toFIGS. 2-4 to describe an example CMTS 22 that bases call admission onmonitoring service flow queues on the CMTS 22. This specific example isnot intended to limit the principles described herein, thus, it shouldbe understood that other examples can implement the principles describedabove differently than the example CMTS 22 shown in FIGS. 2-4.

Before turning to FIG. 2, it should be appreciated that the principlesdescribed above can be applied to any network including otheroversubscription networks such as passive optical networks and DigitalSubscriber Line (DSL) networks. In such other networks, the admissioncontrol can be performed at any gateway for the network.

FIG. 2 illustrates one example of a Cable Modem Termination System(CMTS) according to the system shown in FIG. 1.

The CMTS 22 includes a queue monitor 26 to monitor the depth and/or rateof depth changes in the service flow queues 31-33. The queue monitor 26can monitor the depth and/or rate of depth by inspecting the packets inthe queues, or by identifying delays for the service flow and theninferring/calculating queue depth information based on the identifieddelays (there is a relationship between queue depth and delay, andbetween the rate that the queue fills and the rate of increase indelay). The admission control component 24 uses monitoring results 25indicating current queue depths and/or a rate of changes in the queuedepths to determine whether to admit or deny a new service flow.

A DOCSIS service flow is defined as a unidirectional flow of packetswith a pre-defined Quality of Service (QoS), e.g. rate, bandwidth, etc.These service flows can flow either upstream (from a cable modem to theCMTS 22) or downstream (from the CMTS 22 to a cable modem). Theillustrated service flows 1-3 are based on data received over the datanetwork and flow in the downstream direction to the same or differentcable modems. These service flows 1-3 can represent video streams, voicestreams, etc.

Under DOCSIS, each service flow is assigned a queue on the CMTS 22. Itshould be understood that these queues may be located on the samephysical buffer, but in any case are logically distinct queues. Thequeues are used to buffer receive traffic to be scheduled for downstreamtransmission by a scheduler on the CMTS 22. The scheduler arbitrates therelease of data from the queues according to factors such as whether theservice flow is narrowband or wideband.

The greater the depth of data on a service flow queue, the greater thedelay that will be experienced by an endpoint for the correspondingservice flow. A video could stutter on the endpoint in instances oflarge delay (significant queue depth).

Having described the service flow queues 31-33, it should be appreciatedthat there is one DOCSIS service flow queue per service flow rather thana same receive or transmit buffer pooling traffic from a plurality oflogical connections. The example CMTS 22 utilizes this particularfeature of DOCSIS to estimate QoE on a flow-by-flow basis.

The component 26 monitors the service flow queues 31-33 as indicated bythe curved arrows. The component 26 can monitor instantaneous queuedepth and/or a rate of change in the queue depth. The component 26 feedsthis information 25 to the admission control component 24. As statedpreviously, such monitoring of the service flow queues 31-33 can beconducted in any fashion including determining delay andinferring/calculating queue depth information based on a predictablerelationship between delay and queue depth.

It is possible for a subset of the service flow queues 31-33 to have asignificant queue depth while remaining queues have an insignificantdepth due to the underlying Radio Frequency (RF) channel assignmentsbetween the service flows. As a result, it should be understood thatthere could be significant current delay and/or significantly increasingdelay on one of the service flows 1-3, but not the others. Accordingly,the monitoring results 25 can indicate delay and/or rate of delayincreases on a flow-by-flow basis.

The admission control component 24 analyzes the monitoring results 25and determines whether to admit or deny newly requested service flowsaccordingly. One policy is for the component 24 to deny new requestswhen one of the service flows 1-3 are experiencing delay above a presetthreshold, whether the threshold be a set level of queue depth or a setlevel of change in queue depth over time (queue depth rate). This policycan be particularly useful in some DOCSIS environments because thescheduler (discussed previously) typically operates according to a fairallocation scheme whereby a QoE disruption on one service flow indicatesthat other service flows will soon be affected. Other policies are forthe component 24 to deny new requests when a greater number of theservice flows 1-3 are experiencing delay above the preset threshold.Other policies are for the component 24 to deny new requests when meanor median queue depth or rate for all queues, or a subset of queues, isabove the preset threshold.

The policies discussed above can utilize a historical approach. Forexample, the component 24 can deny new requests when one of the serviceflows experiences a threshold of X instances of delay being above apreset threshold during a set time period T. The other policiesdiscussed above can also utilize a historical approach where thecomponent 24 performs admission based on the rate of QoE disruptions fora given time period T.

The policies discussed above can utilize an averaging approach insteadof, or in addition to, the historical approach. For example, thecomponent 24 can deny new requests when one of the service flowsexperiences an average of N measurements is above a present threshold.The other policies discussed above can also utilize an averagingapproach where the component 24 performs admission based on the rate ofQoE disruptions for a given time period T. The averaging approach is notlimited to mathematically averaging but instead refers to any sort ofaggregation of measurements for a service flow where the measurementsare taken at different times. The aggregation can be performed by thecomponent 26, or the component 24, or both.

One policy that can be used by the component 24 is illustrated by thegraph shown in FIG. 3. Referring to the graph, the x axis indicatesaggregate bandwidth utilization, while the y axis indicates averagedelay for the existing service flows. It has been empirically shown thataverage delay increases in a roughly linear fashion until a “knee” isreached. The knee is often between 80-90% utilization, but in practicethe exact threshold can vary from system to system based on numerousfactors. At the knee, average delay starts to increase in a roughlyparabolic fashion and average QoE starts to be greatly affected by thenew service flows.

It has been empirically discovered that conventional systems usingconservative values for admission control can often stop admitting callsbefore the knee is reached. This essentially wastes resources, becausethe new service flows could have been admitted without any significantdrop in average QoE. In contrast, the component 24 can analyze themonitoring results 25 to determine when an increase in the depth rateindicates that average delay is approaching the knee and continueadmitting the new service flows until average delay starts increasing ina non-linear fashion.

Referring back to FIG. 2, it should be noted that any of these policiescan utilize classification information to group the flows and thenperform admission control according to the groupings. For example, thecomponent 24 could deny additional service flows of a firstclassification, e.g. video or another first type of media, when a QoEdisruption is detected on existing service flows of the firstclassification, but continue to admit service flows of a secondclassification, e.g. voice or another second different type of media, aslong as existing service flows of the second classification do not havethe QoE disruption. In other words, the QoE analysis is performedindependently for different subsets of the service flows. The component24 can obtain the classification information as with any knownclassification engine, such as by directly or indirectly examiningheader data including type/class fields and address fields, or evenpossibly via examining payload data. These classifications can includeany classification such as priority classification including DOCSISdemand values assigned to the flows.

The DOCSIS upstream or downstream scheduler (discussed previously)arbitrates data to the queues 1-3 according to hierarchical queuing. Inone example, the component 24 identifies groups of service flows forpurposes of the QoE analysis on the basis of branches in thehierarchical queuing used by the scheduler operating thereon. In otherwords, service flows of a same branch are in the same grouping for thegroup-based QoE analyses so that the same (or similar groups) are usedby the component 24 and the scheduler.

Admission or denial by the admission control component 24 can includedenying a request for a new service flow, accepting the request withoutchanges to existing service flows, accepting the request withmodifications, accepting the request with modifications to one or moreexisting service flows (e.g. disconnecting a random voice call to findroom for a 911 call or changing a randomly selected voice call fromconstant bit rate to best effort), etc. It should be understood that theadmission control for the CMTS 22 is based on service flow queuemonitoring results, but not necessarily based solely on service flowqueue monitoring results. For example, the component 25 could crosscorrelate these results 25 with other measured information, such as theother packet flow metrics discussed with respect to FIG. 1, to obtain across-correlated estimate of QoE, and then use this cross-correlated QoEestimate for admission control. It may be possible to filter themonitoring results 25, for example delay experienced by a subset of theservice flows (those with a low assigned QoS) could be ignored and/ordiscounted in some way relative to delay experienced by high QoS serviceflows. In any case, the component 24 controls admission according to themonitoring results 25.

It should also be understood that circuitry configured to execute thefunctions described herein can operate on any type of CMTS, includingbut not limited to an Integrated CMTS (I-CMTS) and an Modular (M-CMTS).In the latter example, the circuitry can operate on the core deviceand/or the edge device (including being distributed across the systemformed by the core and edge devices).

It should also be understood that the principles described above are notlimited to DOCSIS networks or even cable networks. Any type of router orother network device can be configured to monitor traffic at anintermediary, estimate QoE at the endpoints according to the monitoringat the intermediary, and perform call admission control according to theQoE estimation.

FIG. 4 illustrates how the CMTS shown in FIG. 2 conducts admissioncontrol according to QoE estimation.

In block 401, the CMTS 22 initializes a queue for each admitted serviceflow on a Cable Modem Termination System (CMTS). These queues areinitialized in conjunction with prior admission of these service flows.

In block 402, the CMTS 22 monitors the queues to generate a plurality ofQuality of Experience (QoE) estimates that each correspond to adifferent subset of the admitted service flows. For example, there couldbe a QoE estimate for each existing service flow. Alternatively, therecould be an average (or otherwise representative) QoE estimate for allservice flows that represent video streams and an average (or otherwiserepresentative) QoE estimate for all service flows that represent voicecalls. Alternatively, there could be a representative QoE estimate forall service flows of a first priority and a representative QoE estimatefor all service flows of a second priority.

In block 403, the CMTS 22 determines whether any of the plurality of QoEestimates indicates a QoE disruption at a corresponding endpoint (orcorresponding endpoints depending on how many service flows correspondto each subset). If a QoE disruption is detected in diamond 404, then inblock 405A the CMTS 22 denies a new service flow based on the detectedQoE disruption. Otherwise, in block 405B, the CMTS 22 admits the newservice flow.

In the above example, the monitoring can be continuous, with thedetermination made in response to receiving a request for the newservice flow. Alternatively, both the monitoring and the determinationcould be performed in response to receiving a request for the newservice flow. Alternatively, both the monitoring and determiningprocesses could be conducted at times that are selected independently ofreceiving the request with the result stored in a register thatindicates whether a QoE disruption was found. In the latter case, theregister setting would remain until the next monitoring and determiningprocess are conducted, and the register would be checked whenever arequest is received for a new service flow.

As previously stated, the CMTS 22 is just one of many examples that canbe implemented according to the principles described herein and inparticular described with reference to FIG. 1. In other examples a CMTScould monitor packet traffic in other ways besides checking queuesassociated with the service flows, as discussed previously. For example,another CMTS could monitor bandwidth utilization of RF channels of theCMTS to estimate QoE and base admission control on such QoE estimation.

It will be apparent to those having skill in the art that many changesmay be made to the details of the above-described embodiments withoutdeparting from the underlying principles of the disclosure. The scope ofthe present disclosure should, therefore, be determined only by thefollowing claims.

Most of the equipment discussed above comprises hardware and associatedsoftware. For example, the typical CMTS or other router is likely toinclude one or more processors and software executable on thoseprocessors to carry out the operations described. We use the termsoftware herein in its commonly understood sense to refer to programs orroutines (subroutines, objects, plug-ins, etc.), as well as data, usableby a machine or processor. As is well known, computer programs generallycomprise instructions that are stored in machine-readable orcomputer-readable storage media. Some embodiments may include executableprograms or instructions that are stored in machine-readable orcomputer-readable storage media, such as a digital memory. We do notimply that a “computer” in the conventional sense is required in anyparticular embodiment. For example, various processors, embedded orotherwise, may be used in equipment such as the components describedherein. The term circuitry used herein can refer to any of the hardwareused to execute a program or routine, or to any hardware that can beused to implement the principles described herein independently ofsoftware.

Memory for storing software again is well known. In some embodiments,memory associated with a given processor may be stored in the samephysical device as the processor (“on-board” memory); for example, RAMor FLASH memory disposed within an integrated circuit microprocessor orthe like. In other examples, the memory comprises an independent device,such as an external disk drive, storage array, or portable FLASH keyfob. In such cases, the memory becomes “associated” with the digitalprocessor when the two are operatively coupled together, or incommunication with each other, for example by an I/O port, networkconnection, etc. such that the processor can read a file stored on thememory. Associated memory may be “read only” by design (ROM) or byvirtue of permission settings, or not. Other examples include but arenot limited to WORM, EPROM, EEPROM, FLASH, etc. Those technologies oftenare implemented in solid state semiconductor devices. Other memories maycomprise moving parts, such as a conventional rotating disk drive. Allsuch memories are “machine readable” or “computer-readable” and may beused to store executable instructions for implementing the functionsdescribed herein.

A “software product” refers to a memory device in which a series ofexecutable instructions are stored in a machine-readable form so that asuitable machine or processor, with appropriate access to the softwareproduct, can execute the instructions to carry out a process implementedby the instructions. Software products are sometimes used to distributesoftware. Any type of machine-readable memory, including withoutlimitation those summarized above, may be used to make a softwareproduct. That said, it is also known that software can be distributedvia electronic transmission (“download”), in which case there willtypically be a corresponding software product at the transmitting end ofthe transmission, or the receiving end, or both.

Having described and illustrated some principles herein with respect tothe detailed examples above, it should be apparent that the inventionmay be modified in arrangement and detail without departing from suchprinciples. We claim all modifications and variations coming within thespirit and scope of the following claims.

1. A method, comprising: measuring traffic associated with a pluralityof service flows processed by a gateway; wherein the plurality ofservice flows includes a first subset and a second different subset, andwherein the traffic measurement approximates Quality of Experience (QoE)separately for the first subset and the second subset; analyzing thetraffic measurement to detect QoE disruption on a subset-by-subsetbasis; performing admission control on the gateway based on theanalysis; and denying a new service flow if the traffic measurementindicates that any one of the service flow subsets have a QoE disruptionregardless of whether other one or ones of the service flow subsets donot have a QoE disruption according to the traffic measurement.
 2. Themethod of claim 1, wherein the gateway is a Cable Modem TerminationSystem (CMTS).
 3. The method of claim 1, further comprising: measuringactual bandwidth utilization for the gateway; comparing the actualbandwidth utilization measurements to bandwidth capacity; anddetermining whether any of the service flow subsets are indicated ashaving a QoE disruption based solely on the bandwidth comparison.
 4. Themethod of claim 1, further comprising performing admission control onthe gateway independently of pre-computed bandwidth estimates for theplurality of service flows.
 5. An apparatus, comprising: a memoryencoded with instructions for execution by one or more processor; andone or more processors coupled to the memory, the one or more processorsbeing operable when executing the instructions to: monitor a pluralityof service flows processed by a Cable Modem Termination System (CMTS) tomeasure traffic for the service flows according to a packet flow metric;wherein the plurality of service flows includes a first subset and asecond different subset, and wherein the traffic measurementapproximates Quality of Experience (QoE) separately for the first subsetand the second subset; analyze the traffic measurement to determinewhether any of the service flow subsets are indicated as having a QoEdisruption by the traffic measurement; and perform admission control onthe CMTS based on the determination; wherein the circuitry is furtherconfigured to deny a new service flow if the traffic measurementindicates that any one of the service flow subsets have a QoE disruptionregardless of whether other one or ones of the service flow subsets donot have a QoE disruption according to the traffic measurement.
 6. Theapparatus of claim 5, wherein the packet flow metric is an actualbandwidth measurement, a service flow queue status, a re-transmissionanalysis, a packet loss analysis, or a one-way transmission delayanalysis.
 7. The apparatus of claim 5, wherein the packet flow metric isan actual bandwidth utilization measurement, and the circuitry isfurther configured to: measure actual bandwidth utilization of RadioFrequency (RF) channels for the CMTS; compare the actual bandwidthutilization measurements to bandwidth capacity; and determine whetherany of the service flow subsets are indicated as having a QoE disruptionbased on the bandwidth comparison.
 8. The apparatus of claim 5, whereinthe circuitry is further configured to admit or deny a new service flowby approving or rejecting a service flow request sent from the CMTS overa packet switched network.
 9. The apparatus of claim 5, wherein a newservice flow is admitted or denied based on the traffic measurement andindependently of pre-computed bandwidth estimates for the monitoredservice flows.
 10. An apparatus, comprising: a memory encoded withinstructions for execution by one or more processor; and one or moreprocessors coupled to the memory, the one or more processors beingoperable when executing the instructions to: monitor a plurality ofservice flows processed by a Cable Modem Termination System (CMTS) tomeasure traffic for the service flows according to a packet flow metric;wherein the plurality of service flows includes a first subset and asecond different subset, and wherein the traffic measurementapproximates Quality of Experience (QoE) separately for the first subsetand the second subset; analyze the traffic measurement to determinewhether any of the service flow subsets are indicated as having a QoEdisruption by the traffic measurement; and perform admission control onthe CMTS based on the determination; wherein the CMTS maintains aseparate service flow queue for each of the monitored service flows, andthe circuitry is further configured to: determine a depth of data on oneof the service flow queues at different times; identify a rate of changein the depth over time according to an analysis of the data depthdeterminations; and perform admission control on the CMTS based on theidentified rate.
 11. An apparatus comprising: a memory encoded withinstructions for execution by one or more processors; and one or moreprocessors coupled to the memory, the one or more processors beingoperable when executing the instructions to: monitor a plurality ofservice flows processed by a Cable Modem Termination System (CMTS) tomeasure traffic for the service flows according to a packet flow metric;wherein the measurement approximates Quality of Experience (QoE)separately for the different service flows such that QoE disruptions canbe identified on a flow-by-flow basis for the plurality of serviceflows; and analyze the measurements to determine whether any of theservice flows have a QoE disruption and perform admission control on theCMTS based on the determination; and wherein the processors are furtheroperable to: deny a new service flow if the measurements indicate thatany one of the monitored service flows have a QoE disruption regardlessof whether other ones of the monitored service flows do not have a QoEdisruption according to the measurements.
 12. An apparatus comprising: amemory encoded with instructions for execution by one or moreprocessors; and one or more processors coupled to the memory, the one ormore processors being operable when executing the instructions to:monitor a plurality of service flows processed by a Cable ModemTermination System (CMTS) to measure traffic for the service flowsaccording to a packet flow metric; wherein the measurement approximatesQuality of Experience (QoE) separately for the different service flowssuch that QoE disruptions can be identified on a flow-by-flow basis forthe plurality of service flows; and analyze the measurements todetermine whether any of the service flows have a QoE disruption andperforming admission control on the CMTS based on the determination;wherein the packet flow metric includes at least one selected from thegroup including a metric representing the status of each service flowqueue for the plurality of service flows, a metric representing anamount of re-transmissions triggered by a Visual Quality of Experience(VQE) agent on a per-service flow basis, a metric representingmonitoring packets dropped by a component of the CMTS on a per-serviceflow basis, and a metric representing one-way packet latency for packetsbeing processed by the CMTS on a per-service flow basis.
 13. Theapparatus of claim 12, wherein the processors are further operable to:measure an amount of bandwidth currently used by all service flows ofthe CMTS; and compare the measured bandwidth to available RadioFrequency (RF) channel bandwidth and performing admission control on theCMTS based on the comparison.
 14. The apparatus of claim 13, wherein theprocessor is configured to admit or deny a new service flow based on thebandwidth measurements and independently of pre-computed bandwidthestimates for the monitored service flows.
 15. An apparatus comprising:a memory encoded with instructions for execution by one or moreprocessors; and one or more processors coupled to the memory, the one ormore processors being operable when executing the instructions to:monitor a plurality of service flows processed by a Cable ModemTermination System (CMTS) to measure traffic for the service flowsaccording to a packet flow metric; wherein the measurement approximatesQuality of Experience (QoE) separately for the different service flowssuch that QoE disruptions can be identified on a flow-by-flow basis forthe plurality of service flows; and analyze the measurements todetermine whether any of the service flows have a QoE disruption andperforming admission control on the CMTS based on the determination;wherein said monitoring includes monitoring service flow queues on theCMTS, and wherein the processors are further operable to: measure adepth of data on one of the service flow queues at different times;identify a rate of change in the depth over time according to ananalysis of the data depth measurements; and perform admission controlon the CMTS based on the identified rate.
 16. The apparatus of claim 15,wherein there is one service flow queue for each service flow, andwherein the processors are further operable to perform the measuring andidentifying for every service flow queue and performing admissioncontrol on the CMTS based on the measuring and identifying for everyservice flow queue.
 17. The apparatus of claim 15, wherein theprocessors are further operable to send the service flow measurementsover a packet switched network to the CMTS.